1. Quantum transfer learning for image classification.
- Author
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Subbiah, Geetha, Krishnakumar, Shridevi S., Asthana, Nitin, Balaji, Prasanalakshmi, and Vaiyapuri, Thavavel
- Subjects
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QUANTUM computing , *BLENDED learning , *QUANTUM computers , *INTEGRATED software , *MACHINE learning , *CLASSIFICATION - Abstract
Quantum machine learning, an important element of quantum computing, recently has gained research attention around the world. In this paper, we have proposed a quantum machine learning model to classify images using a quantum classifier. We exhibit the results of a comprehensive quantum classifier with transfer learning applied to image datasets in particular. The work uses hybrid transfer learning technique along with the classical pre-trained network and variational quantum circuits as their final layers on a small scale of dataset. The implementation is carried out in a quantum processor of a chosen set of highly informative functions using PennyLane a cross-platform software package for using quantum computers to evaluate the high-resolution image classifier. The performance of the model proved to be more accurate than its counterpart and outperforms all other existing classical models in terms of time and competence. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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